•Negated that the sound field of the converter transformer is a point sound source.•Under power frequency and harmonic excitation, there are dipole and multipole sound sources, respectively.•The ...converter transformer under multipole sound sources has higher sound radiation efficiency.•The superposition of two or more harmonics can cause the sound source to transform into a multipole sound source.•The harmonic range that forms a multipole sound source is 5, 7, 11, 13, 23, and 25.
In order to elucidate the sound source characteristics during the noise propagation process of converter transformers, a series of experimental studies were conducted based on finite element simulation and proportional prototype design of converter transformers. It was found that it was unreasonable to equate converter transformers with point sound sources in previous studies. On the contrary, it should be divided into dipole and multipole distribution characteristics. Meanwhile, research has found that the noise distribution of converter transformers exhibits dipole and dipole distribution characteristics under power frequency and harmonic excitation, respectively. The contribution of harmonic components at different frequencies in the formation process of bipolar sound sources was quantified through simulation and experiments, and the key harmonic frequencies for forming multipole sound sources were determined as paired combinations of 5, 7, 11, 13, 23, and 25. Based on the above research conclusions, a noise reduction approach for converter transformers based on bipolar and multipole boundary effects is proposed.
This work focuses on the downlink of a single-cell multi-user system in which a base station (BS) equipped with <inline-formula> <tex-math notation="LaTeX">M </tex-math></inline-formula> antennas ...communicates with <inline-formula> <tex-math notation="LaTeX">K </tex-math></inline-formula> single-antenna users through a reconfigurable intelligent surface (RIS) installed in the line-of-sight (LoS) of the BS. RIS is envisioned to offer unprecedented spectral efficiency gains by utilizing <inline-formula> <tex-math notation="LaTeX">N </tex-math></inline-formula> passive reflecting elements that induce phase shifts on the impinging electromagnetic waves to smartly reconfigure the signal propagation environment. We study the minimum signal-to-interference-plus-noise ratio (SINR) achieved by the optimal linear precoder (OLP), that maximizes the minimum SINR subject to a given power constraint for any given RIS phase matrix, for the cases where the LoS channel matrix between the BS and the RIS is of rank-one and of full-rank. In the former scenario, the minimum SINR achieved by the RIS-assisted link is bounded by a quantity that goes to zero with <inline-formula> <tex-math notation="LaTeX">K </tex-math></inline-formula>. For the high-rank scenario, we develop accurate deterministic approximations for the parameters of the asymptotically OLP, which are then utilized to optimize the RIS phase matrix. Simulation results show that RISs can outperform half-duplex relays with a small number of passive reflecting elements while large RISs are needed to outperform full-duplex relays.
Label information plays an important role in a supervised hyperspectral image classification problem. However, current classification methods all ignore an important and inevitable problem-labels may ...be corrupted and collecting clean labels for training samples is difficult and often impractical. Therefore, how to learn from the database with noisy labels is a problem of great practical importance. In this paper, we study the influence of label noise on hyperspectral image classification and develop a random label propagation algorithm (RLPA) to cleanse the label noise. The key idea of RLPA is to exploit knowledge (e.g., the superpixel-based spectral-spatial constraints) from the observed hyperspectral images and apply it to the process of label propagation. Specifically, the RLPA first constructs a spectral-spatial probability transform matrix (SSPTM) that simultaneously considers the spectral similarity and superpixel-based spatial information. It then randomly chooses some training samples as "clean" samples and sets the rest as unlabeled samples, and propagates the label information from the "clean" samples to the rest unlabeled samples with the SSPTM. By repeating the random assignment (of "clean" labeled samples and unlabeled samples) and propagation, we can obtain multiple labels for each training sample. Therefore, the final propagated label can be calculated by a majority vote algorithm. Experimental studies show that the RLPA can reduce the level of noisy label and demonstrates the advantages of our proposed method over four major classifiers with a significant margin-the gains in terms of the average overall accuracy, average accuracy, and kappa are impressive, e.g., 9.18%, 9.58%, and 0.1043. The MATLAB source code is available at https://github.com/junjun-jiang/RLPA .
Inspired by the recent advances in deep learning, we propose a novel iterative belief propagation - convolutional neural network (BP-CNN) architecture for channel decoding under correlated noise. ...This architecture concatenates a trained CNN with a standard BP decoder. The standard BP decoder is used to estimate the coded bits, followed by a CNN to remove the estimation errors of the BP decoder and obtain a more accurate estimation of the channel noise. Iterating between BP and CNN will gradually improve the decoding SNR and, hence, result in better decoding performance. To train a well-behaved CNN model, we define a new loss function that involves not only the accuracy of the noise estimation but also the normality test for the estimation errors, i.e., to measure how likely the estimation errors follow a Gaussian distribution. The introduction of the normality test to the CNN training shapes the residual noise distribution and further reduces the bit error rate of the iterative decoding, compared to using the standard quadratic loss function. We carry out extensive experiments to analyze and verify the proposed framework. 11 Code is available at https://github.com/liangfei-info/Iterative-BP-CNN.
•Proposed dataset consists of real underwater recordings of 47 h 4 min of 265 ships.•Recordings are from throughout the year with different sea states and noise levels.•Study of 6 T-F features by 8 ...machine learning and deep learning methods on dataset.•Proposed a separable convolutional autoencoder for better classification accuracy.
Underwater acoustic classification is a challenging problem because of presence of high background noise and complex sound propagation patterns in the sea environment. Various algorithms proposed in last few years used own privately collected datasets for design and validation. Such data is not publicly available. To conduct research in this field, there is a dire need of publicly available dataset. To bridge this gap, we construct and present an underwater acoustic dataset, named DeepShip, which consists of 47 h and 4 min of real world underwater recordings of 265 different ships belong to four classes. The proposed dataset includes recording from throughout the year with different sea states and noise levels. The presented dataset will not only help to evaluate the performance of existing algorithms but it shall also benefit the research community in future. Using the proposed dataset, we also conducted a comprehensive study of various machine learning and deep learning algorithms on six time–frequency based extracted features. In addition, we propose a novel separable convolution based autoencoder network for better classification accuracy. Experiments results, which are compared based on classification accuracy, precision, recall, f1-score, and analyzed by using paired sampled statistical t-test, show that the proposed network achieves classification accuracy of 77.53% using CQT feature, which is better than as achieved by other methods.
To effectively operate multivendor disaggregated networks, the performance of the physical layer needs to be assessed by a quality-of transmission estimator (QoT-E) delivering quick results with a ...given reliability range. Current state-of-the-art wavelength-division-multiplexing channels are based on multilevel modulation formats relying on DSP-operated coherent receivers, propagating on uncompensated and amplified optical links. In this transmission scenario, beside amplified spontaneous emission noise accumulation, nonlinear propagation impairments are well summarized by the accumulation of a Gaussian-distributed disturbance: the nonlinear interference (NLI). When exploiting a transmission bandwidth exceeding the C-band, the interaction of NLI generation with the stimulated Raman scattering (SRS) must be properly considered. We present the derivation of the generalized Gaussian noise (GGN) model for NLI generation, including the SRS and, in general, a spectral and spatial variation of gain/loss. We validate its accuracy by comparing performances predicted by a QoT-E based on the GGN model with measurements on a testbed exploiting commercial equipment, including 100 Gb/s transponders. Considering that operational parameters of the commercial equipment are known with a large range of uncertainty, an excellent agreement with errors within 0.5 dB on the generalized Signal-to-noise ratio (<inline-formula><tex-math notation="LaTeX"> \text{SNR}</tex-math></inline-formula>) is shown, demonstrating that the GGN-model can be used for the QoT-E in multivendor network scenarios. Moreover, the GGN model has shown the capability to predict the spectral tilting due to SRS in <inline-formula><tex-math notation="LaTeX">\text{SNR}</tex-math></inline-formula> performances, enabling its application to evaluate the impact of linear pretilting for SRS precompensation and NLI generation.
At present, the main components of quantum communication channels are optical fibers and free space. However, whether it is a fiber channel or a free-space channel, the channel in which it propagates ...is affected by noise, resulting in loss or absorption of photons. Experimental studies have shown that the quantum key can basically meet the requirements of the local area network, and the safety distance is about 100km. However, if you want to achieve longer distance distribution, you can't do without quantum repeaters. The quantum repeater can divide the long-distance communication distance into several segments, and then perform quantum key distribution in several segments respectively; then establish a longer-distance EPR pair between adjacent nodes, and establish an EPR pair to utilize entanglement switching and Entanglement purification technology; finally repeat the above steps, and finally achieve long-distance information transmission. The development of quantum repeaters can solve the communication problems facing the world and promote the replacement of global communication methods.
Channel estimation in vehicle-to-everything (V2X) communications is a challenging issue due to the fast time-varying and non-stationary characteristics of wireless channel. To grasp the complicated ...variations of channel with limited number of pilots in the IEEE 802.11p systems, data pilot-aided (DPA) channel estimation has been widely studied. However, the error propagation in the DPA procedure, caused by the noise and the channel variation within adjacent symbols, limits the performance seriously. In this letter, we propose a deep learning based channel estimation scheme, which exploits a long short-term memory network followed by a multilayer perceptron network to solve the error propagation issue. Simulation results show that the proposed scheme outperforms currently widely-used DPA schemes for the IEEE 802.11p-based V2X communications.
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